code: def plot_scatter(X, y, title):
      """Function to plot some data as a scatter plot."""
      plt.figure()
      plt.scatter(X[y == 1, 0], X[y == 1, 1], label="Class #1")
      plt.scatter(X[y == 0, 0], X[y == 0, 1], label="Class #0")
      plt.legend()
      plt.title(title)
  
file_path: imbalanced-learn/examples/applications/plot_outlier_rejections.py
imports: import matplotlib.pyplot
  import numpy
  from sklearn.datasets import make_blobs, make_moons
  from sklearn.ensemble import IsolationForest
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import classification_report
  from imblearn import FunctionSampler
  from imblearn.pipeline import make_pipeline
label_desc: Use image node_id 1 for calling a global function with example usage: plot_scatter(X, y, title) without return types